# MPhil in Computational Biology

# Brief outline of courses

**Genome Informatics**

*Gos Micklem et al*

Introduction to bioinformatics computing. Entrez, SRS. Molecular databases (GenBank, EMBL, dbSNP, HapMap) and other internet resources for bioinformatics. Genome browsers (NCBI, UCSC, Ensembl). Homology searching (dynamic programming, BLAST). Protein databases (PDB, SwissPROT, Pfam) and tools. Annotation of DNA sequences.

**Structural Biology**

*Julian Huppert, Dima Chirgadze, Xavier Salvatella, David Burke*

Protein structure and function; techniques for protein production. X-ray crystallography, NMR structure determination. Energy minimisation and molecular dynamics. Protein structure prediction. Protein-protein interactions; Structural resources and databases for proteins. Protein folding and aggregation. Nucleic Acid structure and function.

**Functional Genomics**

*Simon Tavaré et al*

Introduction to microarrays: overview of technology, applications and the data generated. Pre-processing, normalisation and quality control for microarrays. Experimental design and planning issues. Analysis of differential expression (DE). Linear models for DE of complex designs. Analysis methods for Affymetrix and Illumina expression data. Probemannotation. Genomic profiling: analysis of aCGH, tiling array, SNP, CNV and ChIP-chip data. Dimension reduction techniques, clustering and classification methods. Meta and survival analysus. Resequencing technologies.

**Disease Dynamics**

*Julia Gog et al*

Mathematical preliminaries, Introduction to population modelling (growth models), population interactions (competition, predation). Introduction to disease modelling, SIR model, SEIR, SIS and other extensions. Spatial models: diffusion networks, metapopulation, pair approximations. Antigenic variation: strain dynamics, antigenic drift models. Examples and applications included influenza, measles, malaria, HIV, rabies.

**Statistical Genetics**

*Simon Tavaré and Vincent Plagnol*

*The Wellcome Trust Case Control Consortium (WTCCC): a case study for case control design.*Genetic epidemiology. Association studies. Transmission Disequilibrium Test (TDT). Coalescent theory. Linkage disequilibrium. Population structure. Generalized linear models. Statistical testing: false positive rate, false discovery rate, statistical power.

**Computational Neuroscience**

*Stephen Eglen*

Introduction to the nervous system: how neurons encode and decode information. Hodgkins-Huxley models of action potential propagation. Introduction to network-level models. Associate networks for long-term storage. Supervised learning methods. Reinforcement learning methods. Unsupervised learning methods. Applications of techniques to understanding visual system development.

**Systems Biology**

*Johan Paulsson and Glen Vinnicombe*

Detecting regulatory networks. Inverse engineering. Scale-free networks.Modelling frameworks: Boolean logic, deterministic rate equations and stochastic processes - analytically and computationally. Kinetic design principles, e.g. feedback loops, metabolic phase transitions, multi-stability, and order versus disorder. Systematic kinetic approaches, e.g. metabolic control analysis and biochemical systems theory. Biological mode systems , e.g. the lac operon, phages, plasmids and chemotaxis. Single cell and single molecule experiments. Synthetic Biology.

**Network Biology**

*Lorenz Wernisch and Simon Tavaré*

Introduction to Network Biology: Biological networks. Topological properties. Network models from dynamic data; Mathematical models: relevance networks, graphical Gaussian models, Bayesian networks. Modelling gene regulatory networks. Metabolic networks. Further statistical tools: factor-analysis, independent component analysi, model selection criteria. Models of alterered pathways in human cancer. Genotype-disease network models.

**Methods and Models in Genomics**

*Pietro Liò*

Computational molecular evolution: Introduction to phylogenetic inference; key concepts in molecular evolution; models of DNA and amino acid; models of codon changes; heterogeneity of rate variatio; detective of positive selection. Parsimony and distance methods; maximum likelihood theory and practice in phylogenetics. Bayesian methods. Statistical methods for phylogeny.